Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.

Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.

Construction and Validation of a Tumor Microenvironment-Based Scoring System to Evaluate Prognosis and Response to Immune Checkpoint Inhibitor Therapy in Lung Adenocarcinoma Patients.

BACKGROUND: Lung cancer is among the most dangerous malignant tumors to human health. Lung adenocarcinoma (LUAD) accounts for about 40% of all lung cancers. Accumulating evidence suggests that the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in LUAD. However, the impact of tumor microenvironment-related signatures (TMERSs) representing the TME characteristics on the prognosis and therapeutic outcome of LUAD patients remains to be further explored. MATERIALS AND METHODS: Gene expression files and clinical information of 1630 LUAD samples and 275 samples with immunotherapy information from different databases such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Cancer Research Institute (CRI) iAtlas were downloaded and analyzed. Three hundred tumor microenvironment-related signatures (TMERS) based on a comprehensive collection of marker genes were quantified by single sample gene set enrichment analysis (ssGSEA), and then eight significant signatures were selected to construct the tumor microenvironment-related signature score (TMERSscore) by performing Least Absolute Shrinkage and Selection Operator (LASSO)-Cox analysis. RESULTS: In this study, we constructed a TME-based prognostic stratification model for patients with LUAD and validated it in several external datasets. Furthermore, the TMERSscore was found to be positively correlated with tumor malignancy and a high TMERSscore predicted a poor prognosis. Moreover, the TMERSscore of responders treated with Immune Checkpoint Inhibitor (ICI) therapies was significantly lower than that of non-responders, and the TMERSscore was positively correlated with the tumor immune dysfunction and exclusion (TIDE) score, implying that a low TMERSscore predicts a better response to ICI treatment and may provide independent and incremental predictive value over current biomarkers. CONCLUSIONS: Overall, we constructed a TMERSscore that can be used for LUAD patient prognosis stratification as well as ICI therapeutic efficacy evaluation, supportive results from independent external validation sets showed its robustness and effectiveness.

Regulatory mechanism of α-hederin upon cisplatin sensibility in NSCLC at safe dose by destroying GSS/GSH/GPX2 axis-mediated glutathione oxidation-reduction system.

Emerging studies showed that α-hederin induced autophagic cell death in different cancers via reactive oxygen species. Nevertheless, α-hederin role in non-small-cell lung cancer (NSCLC) remains unknown. So, the aim of this study was to explain whether ferroptosis is a therapeutic strategy to NSCLC, and to explore the effect of α-hederin on NSCLC ferroptosis. Current investigation found that α-hederin inhibited NSCLC cell proliferation, invasion, and migration in vitro and in vivo at toxic doses. The α-hederin treatment also increased NSCLC cell chemosensitivity to cisplatin and promoted ferroptosis and apoptosis at a safe dose. Proteomics, metabolomics, and high-throughput sequencing detection confirmed that α-hederin treatment downregulated glutathione peroxidase 2 (GPX2), and glutathione synthase (GSS) expression suppressed the synthesis of glutathione (GSH), which destroyed the GSH redox system. Eventually, it led to ferroptosis, apoptosis, and membrane permeabilization in NSCLC. Taken together, the study provided molecular data to confirm that α-hederin induced ferroptosis, apoptosis, and membrane permeabilization in NSCLC by destroying the GSS/GSH/GPX2 axis-mediated GSH oxidation-reduction system at a safe and low-toxicity dose.